针对以往信号处理方法的存在缺陷，提出了一种新的非平稳信号分析方法—稀疏带宽模态分解（Sparse bandwidth mode decomposition，简称SBMD）.该方法将信号分解转化为约束变分问题，自适应地将信号分解为若干个IMF分量之和。另外，在变转速工况下，滚动轴承故障振动信号中含丰富的状态信息，将SBMD、阶次追踪分析和包络谱相结合应用于变转速工况条件下的滚动轴承故障诊断问题。实验分析结果表明，采用SBMD阶次包络谱方法可以及时有效的诊断变转速工况下的滚动轴承故障诊断问题。
Abstract
Target to the defect of previous signal processing method, a new non-stationary signal analysis method, namely the sparse bandwidth mode decomposition (SBMD) is proposed in this paper. The essence of this method is that signal decomposition is converted into constrained variational problem, and the signal is decomposed into a set of IMFs by SBMD. In addition to, the vibration signals of roller bearing with variable speed usually have more comprehensive status information, SBMD is applied to the working condition problem of rolling bearing fault diagnosis under the condition of variable speed combined with, order tracking analysis and envelope spectrum. The analysis results from experimental that SBMD order envelope spectrum approach can handle the problem of roller bearing fault diagnosis under variable speed condition accurately and effectively.
关键词
稀疏带宽模态分解 /
阶次追踪分析 /
包络谱 /
滚动轴承 /
故障诊断
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Key words
Sparse bandwidth mode decomposition /
order tracking analysis /
envelope spectrum /
roller bearing /
fault diagnosis；
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参考文献
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